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Recently, the type of compound regularizers has become a popular choice for signal reconstruction. The estimation quality is generally sensitive to the values of multiple regularization parameters. In this work, based on BDF algorithm, we develop a data-driven optimization scheme based on minimization of Stein's unbiased risk estimate (SURE)— statistically equivalent to mean squared error (MSE). We...
Group sparsity has shown great potential in various low-level vision tasks (e.g, image denoising, deblurring and inpainting). In this paper, we propose a new prior model for image denoising via group sparsity residual constraint (GSRC). To enhance the performance of group sparse-based image denoising, the concept of group sparsity residual is proposed, and thus, the problem of image denoising is translated...
In this paper, we accomplish simultaneous localization and mapping using the monocular LSD-SLAM. This method is different with feature method, estimating the accurate and reconstructing the large scale environment map. Using the direct image alignment, the environment can be mapped in pose-graph of key frames semi-dense maps. The LSD-SLAM contains two advantage. A novel direct tracking method can...
In this paper, we present an efficient single image dehazing approach via scene-adaptive segmentation and improved dark channel model. First, we detect the image depth information and segment the raw image into the close view and distant view. Then, we utilize the minimum channel image of distant view to regularize the atmospheric veil and simultaneously estimate its light value of close view within...
In this paper, we propose a broadband Doppler estimation and compensation receiver, which combines a bi-directional decision feedback equalizer (DFE). Compared with the existing receivers, the proposed closed loop Doppler estimation and compensation receiver combined with a bi-directional DFE can not only estimate and compensate the broadband Doppler symbol-by-symbol, but also reduce the error propagation...
Apparent age estimation from face image has attracted more and more attentions as it is favorable in some real-world applications. In this work, we propose an end-to-end learning approach for robust apparent age estimation, named by us AgeNet. Specifically, we address the apparent age estimation problem by fusing two kinds of models, i.e., real-value based regression models and Gaussian label distribution...
The capacity character of lithium-ion battery is one of the most important performance parameters, which need to be accurate measurement for the safety and efficiency usage. In this paper, the regularity that battery capacity parameter changes with working temperature and charge or discharge rate has been analyzed, and the least squares support vector machine based battery capacity prediction method...
Run-to-Run control methods as applied in semiconductor manufacturing can greatly benefit from partitioning the observed disturbance state into separate components of tool and product disturbance states. Obtaining sufficiently accurate initial estimates of tool and product disturbance terms from historical data greatly improves the effectiveness of this method. Such an estimation method is developed...
To solve remaining useful life prediction problems of nonlinear and non-stationary process of components, a data-driven approach is presented. The approach constructs a state space model (SSM) to describe degradation evolution process; uses extend Kalman filter to estimate state distribution in SSM and take the Expectation-Maximization (EM) algorithm to update parameters. Based on the measured data,...
For the rapid alignment of the ship-borne weapon INS with large initial azimuth attitude error under complex environment disturbances, a nonlinear error propagation model augmented by sensor errors and disturbance sources was proposed. Velocity plus angular rate matching method was applied in the implementation of the alignment. Simulation results show that comparing with the conventional solutions,...
In this paper, a blind bandwidth extension method of audio signals is proposed in which the fine structure of high-frequency information is recovered based on Volterra series. Combining with Gaussian mixture model and codebook mapping to adjust the spectrum envelope and energy gain of the extended high-frequency components separately, the bandwidth of audio signals is extended to super-wideband from...
Acoustic radiation force impulse (ARFI) imaging generates localized small displacements (typically less than 20 microns) in soft tissues and would induce shear waves. The shear wave speed (SWS), which is determined by the tissue elastic properties and can offer quantitative tissue stiffness, can be monitored from the displacement field. Therefore, precise tracking of small displacements is of great...
For conventional bandwidth extension, the spectral patching methods, such as spectral folding, spectral translation and non-linear processing, are employed to reconstruct high frequency signal, yet it leads to the spectral shifting between reconstructed and original signal, and does not retain the original harmonic relations. In this paper, a blind harmonic bandwidth extension method from wideband...
To study the effect of different number of diffusion gradient directions (NDGD) of diffusion tensor imaging (DTI) on dispersion degree of fractional anisotropy (FA) values and its signal noise ratio (SNR) for adult brain tissues. Eight health volunteers were imaged by a 1.5T magnetic resonance scanner with different NDGD (6, 9, 12, 15, 20, 25, and 30 noncollinear) respectively, and seven FA maps associated...
In this paper, considering the characteristics of low SNR on low-field MR systems, we presented an optimized and more effective curve fitting method for quantitative T2 mapping. Series of T2-weighted images were acquired using a multi-echo spin echo sequence on a Siemens 0.35T open MR system. Adaptive curve fitting algorithm was performed based on T2-to-noise-ratio (T2NR) optimization, which was achieved...
Meshless methods popularized in recent years are attractive choices for solving discontinuous and large deformation problems. As one of the most popular methods to form trial function, moving least squares (MLS) can accurately fulfill input signal reconstruction of nonlinear multiple-input multiple-output sensor. However, the parameter matrix obtained from MLS approximation sometimes is ill-conditioned...
Least squares support vector machines (LSSVM), as a recently reported least squares version support vector machines (SVM), involves equality constraints instead of inequality constraints and adopts least squares cost function, therefore it expresses the training by solving a set of linear equations instead of the quadratic programming problem which greatly reduces computational cost. In this paper,...
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